Research map / Image generation / Editing and style transfer
Face Reconstruction from Voice using Generative Adversarial Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| train on seen identities vs test on unseen identities | voice-to-face GAN · voice-to-face matching, unstratified | accuracy (%): 96.83 -> 76.07 | Hurt |
| restrict imposter to same gender (stratified test) | voice-to-face GAN · voice-to-face matching, test | accuracy (%): 76.07 -> 59.69 | Hurt |
| feed noise instead of speech as generator input (ablation) | voice-to-face GAN · discriminator output on 1000 samples | mean discriminator output: 0.09 -> 0.06 | Hurt |
Closest papers by meaning
- Speech2Face: Learning the Face Behind a Voice (CVPR 2019 · similarity 0.90)
- FaceSpeak: Expressive and High-Quality Speech Synthesis from Human Portraits of Different Styles (AAAI 2025 · similarity 0.83)
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- From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speech (CVPR 2025 · similarity 0.82)
- Faces that Speak: Jointly Synthesising Talking Face and Speech from Text (CVPR 2024 · similarity 0.82)
- Let There Be Sound: Reconstructing High Quality Speech from Silent Videos (AAAI 2024 · similarity 0.81)
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